Chromosome 9p21.3 Coordinates Cell Intrinsic and Extrinsic Tumor Suppression

Somatic chromosomal deletions are prevalent in cancer, yet their effects are poorly understood. The most prominent homozygous deletions affect chromosome 9p21.3 and eliminate the CDKN2A/B tumor suppressor genes, thus disabling a cell intrinsic barrier to tumorigenesis. However, half of 9p21.3 deletions encompass a cluster of 16 type I interferons (IFNs) whose co-deletion remains unexplored. To functionally dissect 9p21.3 and other genomic deletions, we developed MACHETE (Molecular Alteration of Chromosomes with Engineered Tandem Elements), a genome engineering strategy that enables flexible modeling of megabase-sized deletions. Applying MACHETE to a syngeneic mouse model of pancreatic cancer, we show that concomitant loss of the IFN cluster with Cdkn2a/b enhanced immune evasion, metastasis, and immunotherapy resistance compared to Cdkn2a/b deletions alone. Mechanistically, IFN co-deletion disrupted type I IFN signaling in the tumor microenvironment, leading to marked changes in infiltrating immune cells and escape from CD8+ T cell surveillance, effects largely driven by the poorly understood interferon epsilon (Ifne). These results reveal how IFN-encompassing 9p21.3 deletions disable cell intrinsic and extrinsic tumor suppression, thereby providing a pervasive route for immune evasion, metastasis, and immunotherapy resistance. Our study provides a framework for interrogating large deletion events in cancer and beyond. RNA-seq analysis was performed from KrasG12D; sgP53; dS or KrasG12D; sgP53; dL tumors, which were harvested from immune-competent mice treated with control IgG or anti-IFNAR1 blocking antibody. Bulk tumor pieces were flash frozen on dry ice and stored at -80C. Tissues were then mechanically disrupted in Trizol and RNA was extracted following manufacturer’s instructions. RNA integrity was analyzed with an Agilent 2100 Bioanalyzer. Samples that passed QC were then used for library preparation and sequencing. Samples were barcoded and run on a HiSeq (Ilumina) in 76 bp SE run, with an average of 50 million reads per sample. RNA-Seq data was then trimmed by removing adapter sequences and reads were aligned to the mouse genome (GRCm38.91; mm10), and transcript counts were used to generate an expression matrix.